The applications of IoT are endless as now
everything connected to internet. It helps us From helping cities predict
accidents and crimes to enabling optimized productivity across industries
through predictive maintenance, IoT(Internet of Things) is creating a treasure
trove of Big Data. Using AI in IoT (Internet of Things is
the only way to keep up with this IoT generated data and gain insights to get
maximum benefit of it.
Present world is facing a rapid expansion of devices and sensors which are connected to the Internet. As a result of it the sheer volume of data that gets created by them is increasing constantly. This data holds great value because it helps in deriving useful insight into what’s working well or what’s not. For example, it can be mentioned that, pointing out conflicts that can arise in industries or providing insights into business risks and opportunities. But the problem arises while finding out ways to analyze large amounts of performance data and information coming from these devices. It is simply not possible for humans to understand and review terabytes of data. For improving speed and correctness of analyzing data coming through sensors enabled devices, AI in IoT is used smartly.
Cognitive Computing in IoT(Internet of Things)
This technology is a subset of AI(Artificial Intelligence) that has the ability of learn things from interactions with humans and their experiences. Cognitive computing is probabilistic; this makes the ability of cognitive systems to keep pace with the complexity and unpredictability of data generated by IoT(Internet of Things) devices. Businesses can use these systems for illuminating aspects of the IoT(Internet of Things) that were previously invisible, such as hidden patterns and insights culled from disparate sources, which allow businesses in making more informed decisions. Not only that cognitive systems can also provide unbiased hypotheses, reasoned arguments and recommendations, which help in generating answers to numerical problems. Smart applications are based on cognitive computing have the ability to understand an organization's goals, and thereby it helps in integrating and analyzing particular data to help businesses achieve those goals.
Machine Learning in IoT(Internet of Things)
Machine learning can be considered as another subfield of AI that deals with the construction and study of systems, which have the ability of learning from data. Machine learning can help companies in taking billions of data points generated by IoT(Internet of Things) devices and boiling them down to what’s really meaningful. Machine learning systems can accurately identify previously known and never-before seen the new patterns. Machine learning holds the promise of discovering correlations and anomalies that have the potential of developing smart applications that can bring improvements across all facets of our day-today lives.
Deep Learning in IoT(Internet of Things)
Deep Learning is a subfield of AI(Artificial Intelligence) and machine learning that consists of a set of algorithms that have the ability to mimic the human brain. Deep Learning algorithms are now applied to several areas such as image recognition, computer vision, pattern recognition, speech recognition, etc. Deep Learning algorithms are best suited for IoT(Internet of Things) devices and smart applications that involve large amounts of data and complex relationships between different parameters. It helps in solving intuitive problems by rejecting inputs that are irrelevant to the solution.
Internet of Things is growing very fast and bringing out various applications. AI can help businesses in understanding what people need from the data generated by human beings. Thus, implementing AI plays an essential role in handling huge amounts of data/information and embedding intelligence in those devices
Present world is facing a rapid expansion of devices and sensors which are connected to the Internet. As a result of it the sheer volume of data that gets created by them is increasing constantly. This data holds great value because it helps in deriving useful insight into what’s working well or what’s not. For example, it can be mentioned that, pointing out conflicts that can arise in industries or providing insights into business risks and opportunities. But the problem arises while finding out ways to analyze large amounts of performance data and information coming from these devices. It is simply not possible for humans to understand and review terabytes of data. For improving speed and correctness of analyzing data coming through sensors enabled devices, AI in IoT is used smartly.
Cognitive Computing in IoT(Internet of Things)
This technology is a subset of AI(Artificial Intelligence) that has the ability of learn things from interactions with humans and their experiences. Cognitive computing is probabilistic; this makes the ability of cognitive systems to keep pace with the complexity and unpredictability of data generated by IoT(Internet of Things) devices. Businesses can use these systems for illuminating aspects of the IoT(Internet of Things) that were previously invisible, such as hidden patterns and insights culled from disparate sources, which allow businesses in making more informed decisions. Not only that cognitive systems can also provide unbiased hypotheses, reasoned arguments and recommendations, which help in generating answers to numerical problems. Smart applications are based on cognitive computing have the ability to understand an organization's goals, and thereby it helps in integrating and analyzing particular data to help businesses achieve those goals.
Machine Learning in IoT(Internet of Things)
Machine learning can be considered as another subfield of AI that deals with the construction and study of systems, which have the ability of learning from data. Machine learning can help companies in taking billions of data points generated by IoT(Internet of Things) devices and boiling them down to what’s really meaningful. Machine learning systems can accurately identify previously known and never-before seen the new patterns. Machine learning holds the promise of discovering correlations and anomalies that have the potential of developing smart applications that can bring improvements across all facets of our day-today lives.
Deep Learning in IoT(Internet of Things)
Deep Learning is a subfield of AI(Artificial Intelligence) and machine learning that consists of a set of algorithms that have the ability to mimic the human brain. Deep Learning algorithms are now applied to several areas such as image recognition, computer vision, pattern recognition, speech recognition, etc. Deep Learning algorithms are best suited for IoT(Internet of Things) devices and smart applications that involve large amounts of data and complex relationships between different parameters. It helps in solving intuitive problems by rejecting inputs that are irrelevant to the solution.
Internet of Things is growing very fast and bringing out various applications. AI can help businesses in understanding what people need from the data generated by human beings. Thus, implementing AI plays an essential role in handling huge amounts of data/information and embedding intelligence in those devices
